

import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


snake_case_         =					{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class  SCREAMING_SNAKE_CASE__   (nn.Module	):





    def __init__( self		,  a):
         super().__init__()
         lowercase__       :  int =			torchvision.models.resnetaaa(pretrained=__UpperCamelCase)
         lowercase__       :  Tuple =			list(model.children())[:-2]
         lowercase__       :  List[str] =			nn.Sequential(*__UpperCamelCase)
         lowercase__       :  Dict =			nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])





    def  snake_case_							( self		,  a):
         lowercase__       :  int =			self.pool(self.model(__UpperCamelCase))
         lowercase__       :  Any =			torch.flatten(__UpperCamelCase		,  start_dim=2)
         lowercase__       :  str =			out.transpose(1		,  2).contiguous()
         return out  # BxNx2048



class  SCREAMING_SNAKE_CASE__   (_UpperCAmelCase	):





    def __init__( self		,  a		,  a		,  a		,  a		,  a):
         lowercase__       :  Union[str, Any] =			[json.loads(__UpperCamelCase) for l in open(__UpperCamelCase)]
         lowercase__       :  List[str] =			os.path.dirname(__UpperCamelCase)
         lowercase__       :  str =			tokenizer
         lowercase__       :  Any =			labels
         lowercase__       :  Any =			len(__UpperCamelCase)
         lowercase__       :  str =			max_seq_length

         lowercase__       :  str =			transforms





    def __len__( self):
         return len(self.data)





    def __getitem__( self		,  a):
         lowercase__       :  int =			torch.LongTensor(self.tokenizer.encode(self.data[index]['text']		,  add_special_tokens=__UpperCamelCase))
         lowercase__ ,					lowercase__ ,					lowercase__       :  Union[str, Any] =			sentence[0], sentence[1:-1], sentence[-1]
         lowercase__       :  Optional[int] =			sentence[: self.max_seq_length]

         lowercase__       :  List[str] =			torch.zeros(self.n_classes)
         lowercase__       :  Optional[int] =			1

         lowercase__       :  List[Any] =			Image.open(os.path.join(self.data_dir		,  self.data[index]['img'])).convert('RGB')
         lowercase__       :  List[str] =			self.transforms(__UpperCamelCase)

         return {
             "image_start_token": start_token,
             "image_end_token": end_token,
             "sentence": sentence,
             "image": image,
             "label": label,
         }





    def  snake_case_							( self):
         lowercase__       :  Tuple =			Counter()
         for row in self.data:
              label_freqs.update(row['label'])
         return label_freqs



def       snake_case__     (       SCREAMING_SNAKE_CASE_						:       Any     ):



     '''simple docstring'''





     lowercase__       :  Tuple =			[len(row['sentence']     ) for row in batch]
     lowercase__ ,					lowercase__       :  Any =			len(_A     ), max(_A     )

     lowercase__       :  Any =			torch.zeros(_A      ,	_A      ,	dtype=torch.long     )
     lowercase__       :  List[str] =			torch.zeros(_A      ,	_A      ,	dtype=torch.long     )

     for i_batch, (input_row, length) in enumerate(zip(_A      ,	_A     )     ):
          lowercase__       :  int =			input_row['sentence']
          lowercase__       :  int =			1

     lowercase__       :  Any =			torch.stack([row['image'] for row in batch]     )
     lowercase__       :  int =			torch.stack([row['label'] for row in batch]     )
     lowercase__       :  Tuple =			torch.stack([row['image_start_token'] for row in batch]     )
     lowercase__       :  int =			torch.stack([row['image_end_token'] for row in batch]     )

     return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor

def       snake_case__     (       ):



     '''simple docstring'''





     return [
         "Crime",
         "Drama",
         "Thriller",
         "Action",
         "Comedy",
         "Romance",
         "Documentary",
         "Short",
         "Mystery",
         "History",
         "Family",
         "Adventure",
         "Fantasy",
         "Sci-Fi",
         "Western",
         "Horror",
         "Sport",
         "War",
         "Music",
         "Musical",
         "Animation",
         "Biography",
         "Film-Noir",
     ]

def       snake_case__     (       ):



     '''simple docstring'''





     return transforms.Compose(
         [
             transforms.Resize(256     ),
             transforms.CenterCrop(224     ),
             transforms.ToTensor(),
             transforms.Normalize(
                 mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]      ,	std=[0.1222_1994, 0.1214_5835, 0.1438_0469]      ,	),
         ]     )



"""simple docstring"""



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


a       :						List[str]       		=		logging.get_logger(__name__)




class 		a_    :







    def __init__(      self      :							List[str]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Tuple  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = question_encoder
        _UpperCAmelCase        = generator
        _UpperCAmelCase        = self.question_encoder







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							str  )		->Dict:






        '''simple docstring'''






        if os.path.isfile(__UpperCamelCase  ):
            raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""  )
        os.makedirs(__UpperCamelCase  ,       exist_ok=__UpperCamelCase  )
        _UpperCAmelCase        = os.path.join(__UpperCamelCase  ,       """question_encoder_tokenizer"""  )
        _UpperCAmelCase        = os.path.join(__UpperCamelCase  ,       """generator_tokenizer"""  )
        self.question_encoder.save_pretrained(__UpperCamelCase  )
        self.generator.save_pretrained(__UpperCamelCase  )







    @classmethod
    def   _snake_case       (      cls      :							Optional[Any]  ,       __UpperCamelCase      :							Tuple  ,       **__UpperCamelCase      :							Optional[Any]  )		->Tuple:






        '''simple docstring'''






        from ..auto.tokenization_auto import AutoTokenizer

        _UpperCAmelCase        = kwargs.pop("""config"""  ,       __UpperCamelCase  )

        if config is None:
            _UpperCAmelCase        = RagConfig.from_pretrained(__UpperCamelCase  )

        _UpperCAmelCase        = AutoTokenizer.from_pretrained(
            __UpperCamelCase  ,       config=config.question_encoder  ,       subfolder="""question_encoder_tokenizer"""  )
        _UpperCAmelCase        = AutoTokenizer.from_pretrained(
            __UpperCamelCase  ,       config=config.generator  ,       subfolder="""generator_tokenizer"""  )
        return cls(question_encoder=__UpperCamelCase  ,       generator=__UpperCamelCase  )







    def __call__(      self      :							Any  ,       *__UpperCamelCase      :							List[Any]  ,       **__UpperCamelCase      :							Optional[Any]  )		->Optional[Any]:






        '''simple docstring'''






        return self.current_tokenizer(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  ,       *__UpperCamelCase      :							List[Any]  ,       **__UpperCamelCase      :							Tuple  )		->Any:






        '''simple docstring'''






        return self.generator.batch_decode(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Optional[int]  ,       *__UpperCamelCase      :							Optional[int]  ,       **__UpperCamelCase      :							List[str]  )		->Union[str, Any]:






        '''simple docstring'''






        return self.generator.decode(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.question_encoder







    def   _snake_case       (      self      :							Tuple  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.generator







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[List[str]] = None  ,       __UpperCamelCase      :							Optional[int] = None  ,       __UpperCamelCase      :							Optional[int] = None  ,       __UpperCamelCase      :							str = "longest"  ,       __UpperCamelCase      :							str = None  ,       __UpperCamelCase      :							bool = True  ,       **__UpperCamelCase      :							Tuple  ,       )		->BatchEncoding:






        '''simple docstring'''






        warnings.warn(
            """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
            """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
            """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
            """details"""  ,       __UpperCamelCase  ,       )
        if max_length is None:
            _UpperCAmelCase        = self.current_tokenizer.model_max_length
        _UpperCAmelCase        = self(
            __UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       return_tensors=__UpperCamelCase  ,       max_length=__UpperCamelCase  ,       padding=__UpperCamelCase  ,       truncation=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        if tgt_texts is None:
            return model_inputs
        # Process tgt_texts
        if max_target_length is None:
            _UpperCAmelCase        = self.current_tokenizer.model_max_length
        _UpperCAmelCase        = self(
            text_target=__UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       return_tensors=__UpperCamelCase  ,       padding=__UpperCamelCase  ,       max_length=__UpperCamelCase  ,       truncation=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        _UpperCAmelCase        = labels["""input_ids"""]
        return model_inputs